Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks 5 Even though, credibility of stochastic simulation has been questioned when applied to practical problems, mainly due to the application of not robust methodology for simulation projects, which should comprise at least the following: – The correct definition of the problem. – An accurate design of the conceptual model. – The formulation of inputs, assumptions, and processes definition. – Build of a valid and verified model. – Design of experiments. – Proper analysis of the simulation output data. 3. Model credibility 3.1 Problem definition To formulate a problem is so important as to solve it. There is a claim credited to Einstein that states: ”The formulation of a problem is often more essential than its solution, which may be merely a matter of mathematical or experimental skill”. The comprehension of how the system works and what are the main specific questions the experimenter wants to investigate, will drive the decisions of which performance measures are of real interest. Experts are of the opinion that the experimenter should write a list of the specific questions the model will address, otherwise it will be difficult to determine the appropriate level of details the simulation model will have. As simulation’s detail increases, development time and simulation execution time also increase. Omitting details, on the other hand, can lead to erroneous results. (Balci & Nance, 1985) formally stated that the verification of the problem definition is an explicit requirement of model credibility, and proposed high-level procedure for problem formulation, and a questionnaire with 38 indicators for evaluating a formulated problem. 3.2 Sources of randomness The state of a WMN can be described by a stochastic or random process, that is nothing but a collection of random variables observed along a time window. So, input variables of a WMN simulation model, such as the transmission range of each WMC, the size of each packet transmitted, the packet arrival rate, the duration of periods ON an OFF of a VoIP source, etc, are random variables that need to be: 1. Precisely defined by means of measurements or well-established assumptions. 2. Generated with its specific probability distribution, inside the simulation model during execution time. The generation of a random variate - a particular value of a random variable - is based on uniformly distributed random numbers over the interval [0, 1), the elementary sources of randomness in stochastic simulation. In fact, they are not really random, since digital computers use recursive mathematical relations to produce such numbers. Therefore, it is more appropriate to call then pseudo-random numbers (PRNs). Pseudo-random numbers generators (PRNGs) lie in the heart of any stochastic simulation methodology, and one must be sure that its cycle is long enough in order to avoid any kind of correlation among the input random variables. This problem is accentuated when there is 189 Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks 6 Wireless Mesh networks a large number of random variables in the simulation model. Care must be taken concerning PRNGs with small periods, since with the growth of CPU frequencies, a large amount of random numbers can be generated in a few seconds (Pawlikowski et al., 2002). In this case, by exhausting the period, the sequence of PRNs will be soon repeated, yielding then correlated random variables, and compromising the quality of the results. As the communication systems become even more sophisticated, their simulations require more and more pseudo-random numbers which are sensitive to the quality of the underlying generators (L’Ecuyer, 2001). One of the most popular simulation packages for modeling WMN is the so called ns-2 (Network Simulator) (McCanne & Floyd, 2000). In 2002, (Weigle, 2006) added an implementation of the MRG32k3, a combined multiple recursive generator (L’Ecuyer, 1999), since it has a longer period, and provides a larger number of independent PRNs substreams, which can be assigned to different random variables. This is a very important issue, and could be verified before using a simulation package. We have been encouraging our students to test additional robust PRNGs, such as Mersenne Twister (Matsumoto & Nishimura, 1998) and Quantum Random Bit Generator – QRBG (Stevanovi ´ c et al., 2008). 3.3 Valid model Model validation is the process of establishing whether a simulation model possesses a satisfactory range of accuracy consistent with the real system being investigated, while model verification is the process of ensuring that the computer program describing the simulations is implemented correctly. Being designed to answer a variety of questions, the validity of the model needs to be determined with respect to each question, that is, a simulation model is not a universal representation of a system, but instead it should be an accurate representation for a set of experimental conditions. So, a model may be valid for one set of experimental conditions and invalid for another. Although it is a mandatory task, it is often time consuming to determine that a simulation model of a WMN is valid over the complete domain of its intended applicability. According to (Law & McComas, 1991), this phase can take about 30%–40% of the study time. Tests and evaluations should be conducted until sufficient confidence is obtained and a model can be considered valid for its intended application (Sargent, 2008). A valid simulation model for a WMN is a set of parameters, assumptions, limitations and features of a real system. This model must also address the occurrence of errors and failures inherent, or not, to the system. This process must be carefully conducted to not introduce modeling errors. It should be a very good practice to present the validation of the used model, and the corresponding deployed methodology so independent experimenters can replicate the results. Validation against a real-world implementation, as advocated by (Andel & Yasinac, 2006), it is not always possible, since the system might not even exist. Moreover, high fidelity, as said previously, is often time consuming, and not flexible enough. Therefore, (Sargent, 2008) suggests a number of pragmatic validation techniques, which includes: – Comparison to other models that have already been validated. – Comparison to known results of analytical models, if available. – Comparison of the similarity among corresponding events of the real system. – Comparison of the behavior under extreme conditions. – Trace the behavior of different entities in the model. 190 Wireless Mesh Networks Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks 7 – Sensitivity analysis, that is, the investigation of potential changes and errors due changes in the simulation model inputs. For the sake of example, Ivanov and colleagues (Ivanov et al., 2007) presented a practical example of experimental results validation of a wireless model written with the Network Simulator (McCanne & Floyd, 2000) package for different network performance metrics. They followed the approach from (Naylor et al., 1967), to validate the simulation model of a static ad-hoc networks with 16 stations by using the NS-2. The objective of the simulation was to send a MPEG4 video stream from a sender node to a receiving node, with a maximum of six hops. The validation methodology is composed of three phases: Face validity This phase is based on the aid of experienced persons in the field, together with the observation of real system, aiming to achieve high degree of realism. They chose the more adequate propagation model and MAC parameters, and by means of measurements on the real wireless network, they found the values to set up those parameters. Validation of Model Assumption In this phase, they validated the assumptions of the shadowing propagation model by comparing model-generated and measured signal power values. Validation of input-output transformation In this phase, they compared the outputs collected from the model and the real system. 3.4 Design of experiments To achieve full credibility of a WMN simulation study, besides developing a valid simulation model, one needs exercise it in valid experiments in order to observe its behavior and draw conclusions on the real network. Careful planning of what to do with the model can save time and efforts during the investigation, making the study efficient. Documentation of the following issues can be regarded as a robust practice. Purpose of the simulation study The simple statement of this issue will drive the overall planning. Certainly, as the study advances and we get deeper understanding of the system, the ultimate goals can be improved. Relevant performance measures By default, most simulation packages deliver a set of responses that could be avoided if they are not of interest, since the corresponding time frame could be used to expand the understanding of the subtleties of WMN configurations. Type of simulation Sometimes, the problem definition constraints our choices to the deployment of terminating simulation. For example, by evaluating the speech quality of a VoIP transmission over a WMN, we can choose a typical conversation duration of 60 seconds. So, there is no question about starting or stopping the simulation. A common practice is to define the number of times the simulation will be repeated, write down the intermediate results, and average them at the end of the overall executions. We have been adopting a different approach based on steady-state simulation approach. To mitigate the problem of initialization bias, we rely on Akaroa 2.28 (Ewing et al., 1999) to determine the length of the warm-up period, during which data collected during are not representative of the actual average values of the parameters being simulated, and cannot be used to produce good estimates of steady-state parameters. To rely on arbitrary choices for the run length of the simulation is an unacceptable practice, which compromises the credibility of the entire study. 191 Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks 8 Wireless Mesh networks Experimental Design The goal of a proper experimental design is to obtain the maximum information with the minimum number of experiments. A factor of an experiment is a controlled independent variable, whose levels are set by the experimenter. The factors can range from categorical factors such as routing protocols to quantitative factors such as network size, channel capacity, or transmission range (Totaro & Perkins, 2005). It is important to understand the relationship between the factors since they impact strongly the performance metrics. Proper analysis requires that the effects of each factor be isolated from those of others so that meaningful statements can be made about different levels of the factor. As a simple checklist for this analysis, we can enumerate: 1. Define the factors and their respective levels, or values, they can take on; 2. Define the variables that will be measured to describe the outcome of the experimental runs (response variables), and examine their precision. 3. Plan the experiments. Among the available standard designs, choose one that is compatible with the study objective, number of design variables and precision of measurements, and has a reasonable cost. Factorial designs are very simple, though useful in preliminary investigation, especially for deciding which factors are of great impact on the system response (the performance metric). The advantages of factorial designs over one-factor-at-a-time experiments is that they are more efficient and they allow interactions to be detected. To thoroughly know the interaction among the factors, a more sophisticated design must be used. The approach adopted in (C.L.Barrett et al., 2002) is enough in our problem of interest. The authors setup a factorial experimental design to characterize the interaction between the factors of a mobile ad-hoc networks such as MAC, routing protocols, and nodes’ speed. To characterize the interaction between the factors, they used ANOVA (analysis of variance), a well-known statistical procedure. 3.5 Output analysis A satisfactory level of credibility of the final results cannot be obtained without assessing their statistical errors. Neglecting the proper statistical analysis of simulation output data cannot be justified by the fact that some stochastic simulation studies might require sophisticated statistical techniques. A difficult issue is the nature of the output observations of a simulation model. Observations collected during typical stochastic simulations are usually strongly correlated, and the classical settings for assessing the sample variance cannot be applied directly. Neglecting the existence of statistical correlation can result in excessively optimistic confidence intervals. For a thorough treatment of this and related questions, please refer to (Pawlikowski, 1990). The ultimate objective of run length control is to terminate the simulation as soon as the desired precision of relative width of confidence interval is achieved. There is a trade-off since one needs a reasonable amount of data to get the desired accuracy, but on the other hand this can lengthen the completion time. Considering that early stopping leads to inaccurate results, it is mandatory to decrease the computational demand of simulating steady-state parameters (Mota, 2002). Typically, the run length of a stochastic simulation experiment is determined either by assigning the amount of simulation time before initiating the experiment or by letting the simulation run until a prescribed condition occurs. The latter approach, known as sequential 192 Wireless Mesh Networks Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks 9 procedure, gather observations at the output of the simulation model to investigate the performance metrics of interest, and a decision has to be taken to stop the sampling. It is evident that the number of observations required to terminate the experiment is a random variable since it depends on the outcome of the observations. According to this thought, carefully-designed sequential procedures can be economical in the sense that we may reach a decision earlier compared to fixed-sample-sized experiments. Additionally, to decrease computational demands of intensive stochastic simulation one can dedicate more resources to the simulation experiment by means of parallel computing. Efficient tools for automatically analyzing simulation output data should be based on secure and robust methods that can be broadly and safely applied to a wide range of models without requiring from simulation practitioners highly specialized knowledge. To improve the credibility of our simulation to investigate the proposal of using bandwidth efficiently for carrying VoIP over WMN, we used a combination of these approaches, namely, we applied a sequential procedure based on spectral analysis (Heidelberger & Welch, 1981) under Akaroa-2, an environment of Multiple Replications in Parallel (MRIP) (Ewing et al., 1999). Akaroa-2 enables the same sequential simulation model be executed in different processors in parallel, aiming to produce independent an identically distributed observations by initiating each replication with strictly non-overlapping streams of pseudo-random numbers. It controls the run length and the accuracy of final results. This environment solve automatically some critical problems of stochastic simulation of complex systems: 1. Minimization of bias of steady-state estimates caused by initial conditions. Except for regenerative simulations, data collected during transient phase are not representative of the actual average values of the parameters being simulated, and cannot be used to produce good estimates of steady-state parameters. The determination of its length is a challenging task carried out by a sequential procedure based on spectral analysis. Underestimation of the length of the transient phase leads to bias in the final estimate. Overestimation, on the other hand, throws away information on the steady state and this can increase the variance of the estimator. 2. Estimation of the sample variance of a performance measure and its confidence interval in the case of correlated observations in equilibrium state; 3. Stopping the simulation within a desired precision selected by the experimenter. Akaroa-2 was designed for full automatic parallelization of common sequential simulation models, and full automated control of run length for accuracy of the final results Ewing et al. (1999). An instance of a sequential simulation model is launched on a number of workstations (operating as simulation engines) connected via a network, and a central process takes care of collecting asynchronously intermediate estimates from each processor and calculates conveniently an overall estimate. The only things synchronized in Akaroa-2 are substreams of pseudo-random numbers to avoid overlapping among them, and the load of the same simulation model into the memory of different processors, but in general this time can be considered negligible and imposes no obstacle. Akaroa-2 enables the same simulation model be executed in different processors in parallel, aiming to produce IID observations by initiating each replication with strictly non-overlapping streams of pseudo-random numbers provided by a combined multiple recursive generator (CMRG) (L’Ecuyer, 1999). 193 Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks 10 Wireless Mesh networks Essentially, a master process (Akmaster) is started on a processor, which acts as a manager, while one or more slave processes (akslave) are started on each processor that takes part in the simulation experiment, forming a pool of simulation engines (see Figure 2). Akaroa-2 takes care of the fundamental tasks of launching the same simulation model on the processors belonging to that pool, controlling the whole experiment and offering an automated control of the accuracy of the simulation output. At the beginning, the stationary Schruben test (Schruben et al., 1983) is applied locally within each replication, to determine the onset of steady state conditions in each time-stream separately and the sequential version of a confidence interval procedure is used to estimate the variance of local estimators at consecutive checkpoints, each simulation engine following its own sequence of checkpoints. Each simulation engine keeps on generating output observations, and when the amount of collected observations is sufficient to yield a reasonable estimate, we say that a checkpoint is achieved, and it is time the local analyzer to submit an estimate to the global analyzer, located in the processor running akmaster. The global analyzer calculates a global estimate, based on local estimates delivered by individual engines, and verifies if the required precision was reached, in which case the overall simulation is finished. Otherwise, more local observations are required, so simulation engines continue their activities. Whenever a checkpoint is achieved, the current local estimate and its variance are sent to the global analyzer that computes the current value of the global estimate and its precision. NS-2 does not provide support for statistical analysis of the simulation results, but in order to control the simulation run length, ns-2 and Akaroa-2 can be integrated. Another advantage of this integration is the control of achievable speed-up by adding more processors to be run Engine Simulation Engine Simulation Engine Simulation Manager Simulation Analyser Local Local Analyser Analyser Local akmaster process Global Analyser akrun process Host 1 Host 2 Host 3 Fig. 2. Schematic diagram of Akaroa. 194 Wireless Mesh Networks Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks 11 in parallel. A detailed description of this integration can be found in (The ns-2akaroa-2 Project, 2001). 4. Case study: header compression 4.1 Problem definition One of the major challenges for wireless communication is the capacity of wireless channels, which is especially limited when a small delay bound is imposed, for example, for voice service. VoIP signaling packets are typically large, which in turn could cause a long signaling and media transport delay when transmitted over wireless networks (Yang & Wang, 2009). Moreover, VoIP performance in multi-hop wireless networks degrades with the increasing number of hops (Dragor et al., 2006). VoIP packets are divided into two parts, headers and payload, that travel on RTP protocol over UDP. The headers are control information added by the underlying protocols, while the payload is the actual content carried out by the packet, that is, the voice encoded by some codec. As Table 1 shows, most of the commonly used codecs generates packets whose payload is smaller than IP/UDP/RTP headers (40 bytes). In order to use the wireless channel capacity efficiently and make VoIP services economically feasible, it is necessary to apply compression techniques to reduce the overheads in the VoIP bearer and signaling packets. The extra bandwidth spared from control information traffic can be used to carry more calls in the same wireless channel or to allow the use of better quality codec to encode the voice flow. Header compression in WMNs can be implemented in the mesh routers. Every packet received by a router from a mesh client should be compressed before being forwarded to the mesh backbone, and each packet forwarded to a mesh client should be decompressed before being forwarded out of the backbone. This guarantees that only packets with compressed headers would be transported among mesh backbone routers. Header compression is implemented by eliminating redundant header information among packets of the same flow. The eliminated information is stored into data structures on the compressor and the decompressor, named context. When compressor and decompressor are under synchronization, it means that both compressor context and decompressor context are updated with the header information of the last sent/received packet of the flow. Figure 3 shows the scheme of header compression. Bit rate Packet duration Payload size Codec (kbps) (ms) (bytes) G.711 64.0 20 160 G.726 32.0 20 80 G.728 16.0 20 40 G.729a 8.0 20 20 G.723.1 (MP-MLQ) 6.3 30 24 G.723.1 (ACELP) 5.3 30 20 GSM-FR 13.2 20 33 iLBC 13.33 30 50 iLBC 15.2 20 38 Table 1. Payload size generated by the most used codecs. 195 Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks 12 Wireless Mesh networks Fig. 3. General header compression scheme. When a single packet is lost, the compressor context will be updated but the decompressor context will not. This may lead the decompressor to perform an erroneous decompression, causing the loss of synchronization between the edges and lead to the discard of all following packets at the decompressor until synchronization is restored. This problem may be crucial to the quality of communication on highly congested environments. WMNs offer a high error rate in the channel due to the characteristics of the transmission media. Since only a device can transmit at a time, when more than one element transmits at the same time a collision occurs, as in the problem of the hidden node, which can result in loss of information in both transmitters. Moreover, many other things can interfere with communication, as obstacles in the environment, and receiving the same information through different paths in the propagation medium (multi-path fading). With these characteristics, the loss propagation problem may worsen, and the mechanisms of failure recovery by the algorithms may not be sufficient, especially in the case of bursty loss. Furthermore, the bandwidth in wireless networks is limited, making the allowed number of simultaneous users also limited. The optimal use of available bandwidth can maximize the number of users on the network. 4.2 Robust header compression – RoHC The Compressed RTP (CRTP) was the first proposed header compression algorithm for VoIP, defined in the Request for Comments (RFC) 2508 (Casner & Jacobson, 1999). It was originally developed for low-speed serial links, where real-time voice and video traffic is potentially problematic. The algorithm compresses IP/UDP/RTP headers, reducing their Fig. 4. Loss propagation problem. 196 Wireless Mesh Networks Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks 13 size to approximately 2 bytes when the UDP checksum header is not present, and 4 bytes otherwise. CRTP was designed based on the unique header compression algorithm available until that date, the Compressed TCP (CTCP) Jacobson (1990), which defines a compression algorithm for IP and TCP headers in low-speed links. The main feature of CRTP is the simplicity of its mechanism. The operation of CRTP defines sending a first message with all the original headers information (FULL HEADER), used to establish the context in the compressor and decompressor. Then, the headers of following packets are compressed and sent, carrying only the delta information of dynamic headers. FULL HEADER packets are also periodically sent to the decompressor, in order to maintain synchronization between the contexts, or when requested by the decompressor through a feedback channel, if the decompressor detects that there was a context synchronization loss. CRTP does not present a good performance over wireless networks, since it was originally developed for reliable connections (Koren et al., 2003), and characteristic of wireless networks present high packet loss rates. This is because the CRTP does not offer any mechanism to recover the system from a synchronization loss, presenting the loss propagation problem. The fact that wireless networks do not necessarily offers a feedback channel available to request for context recovery also influences the poor performance of CRTP. The Robust Header Compression (RoHC) algorithm (Bormann et al., 2001) and (Jonsson et al., 2007) was developed by the Internet Engineering Task Force (IETF) to offer a more robust mechanism in comparison to the CRTP. RoHC offers three operating modes: unidirectional mode (U-mode), bidirectional optimistic mode (O-mode) and bidirectional reliable mode (R-mode). Bidirectional modes make use of a feedback channel, as well as the CRTP, but the U-mode defines communication from the compressor to the decompressor only. This introduces the possibility of using the algorithm over links with no feedback channel or where it is not desirable to be used. The U-mode works with periodic context updates through messages with full headers sent to the decompressor. The B-mode and R-mode work with request for context updates made by the decompressor, if a loss of synchronization is detected. The work presented in (Fukumoto & Yamada, 2007) showed that the U-mode is most advantageous for wireless asymmetrical links, because the context update does not depend on the request from the decompressor through a channel that may not be available (by the fact that it is asymmetric link). The RoHC algorithm uses a method of encoding for the values of dynamic headers that are transmitted in compressed headers, called Window-Least Significant Bits (W-LSB). This encoding method is used for headers that present small changes. It encodes and sends only the least significant bits, which the decompressor uses to calculate the original value of the header together with stored reference values (last values successfully decompressed). This mechanism, by using a window of reference values, provides a certain tolerance to packet loss, but if there is a burst loss that exceeds the window width, the synchronization loss is unavoidable. To check whether there is a context synchronization loss, the RoHC implements a check on the headers, called Cyclic Redundancy Check (CRC). Each compressed header has a header field that carries a CRC value calculated over the original headers before the compression process. After receiving the packet, the decompressor retrieves the headers values with the information from the compressed header and from its context, and executes again the calculation of the CRC. If the value equals the value of the CRC header field, then the compression is considered 197 Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks 14 Wireless Mesh networks successful, otherwise it is found a synchronization loss. The RoHC offers a high compression degree, and high robustness, but its implementation is quite complex compared to other algorithms. Furthermore, RoHC has been implemented for cellular networks, which typically have one single wireless link, and it considers that the network delivers packets in order. 4.3 Static compression + aggregation A header compression algorithm that does not need synchronization of contexts could eliminate any possibility of discarding packets at the decompressor due to packet loss, and eliminate all necessary process for updating and context re-synchronization. However, the cost to implement such an algorithm may be reflected in the compression gain, which may be lower with respect to algorithms that require synchronization. If it is not possible to maintain the synchronization, the decompressor cannot decompress the headers of received packets. Whereas usually the decompressor is based on the information of previously received packets of the same stream to update its context, the loss of a single packet can result in the context synchronization loss, and then the decompressor may not decompress the following packets successfully, even if they arrive on time and without errors at the decompressor, and it is obliged to discard them. In this case we say that the loss was propagated as the loss of a single packet leads to the decompressor to discard all the following packets (Figure 4). To alleviate the loss propagation problem, some algorithms use context update messages. Those messages are sent periodically, containing all the complete information of the headers. When the decompressor receives an update message, it replaces the entire contents of its current context for the content of the update message. If it is unsynchronized, it will use the information received to update its reference values, and thus restore the synchronization. One way to solve the problem of discarding packets at the decompressor due to context desynchronization was proposed in (Nascimento, 2009), by completely eliminating the need of keeping synchronization between compressor and decompressor. The loss propagation problem can be eliminated through the implementation of a compression algorithm whose contexts store only the static headers, and not the dynamic ones. If the contexts store static information only, there is no need for synchronization. This type of compression is called static compression. The static compression has the advantage of no need of updating the context of compressor and decompressor. It only stores the static information, i.e., those that do not change during a session. This means that no packet loss will cause following packets to be discarded at the decompressor, thus eliminating the loss propagation problem. Another advantage presented by the static compression is the decrease in the amount of information to be stored in points where compression and decompression occur, as the context stores only the static information. However, the cost of maintaining contexts without the need for synchronization is reflected in the compression gain, since the dynamic information is sent in the channel and is not stored in context, as in conventional algorithms (Westphal & Koodli, 2005). This causes the compressed header size increase in comparison to conventional compression algorithms headers size, reducing the compression gain achieved. The static compression can reduce the headers size to up to 35% of its original size. Some conventional algorithms, which require synchronization, can reduce the headers size to less than 10%. Experiments with static compression in this work showed that even though this algorithm does not present the loss propagation problem, its compression gain is not large 198 Wireless Mesh Networks [...]... Evaluation of VoIP Over Wireless Mesh Networks Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks 23 207 Society, Virginia, USA, pp 95 –105 Lili, Z., Huibin, W., Lizhong, X., Zhuoming, X & Chenming, L (20 09) Fault tolerance and transmission delay in wireless mesh networks, NSWCTC ’ 09: Proceedings of the 20 09 International Conference on Networks Security, Wireless Communications... systems, IEEE Symposium on Computers and Communications, Cartagena, Spain, pp 93 3 93 8 Casner, S & Jacobson, V ( 199 9) Compressing IP/UDP/RTP Headers for Low-Speed Serial Links, Request for Comments 2508 Clark, A D (2003) Modeling the Effects of Burst Packet Loss and Recency on Subjective Voice 22 206 Wireless Mesh networks Wireless Mesh Networks Quality, IP Telephony Workshop, Columbia University C.L.Barrett,... VoIP applications, Int J Commun Syst 19( 3): 299 –316 IEEE (2004) IEEE 802.11TM Wireless Local Area Networks URL: http://grouper.ieee.org/groups/802/11/ Ivanov, S., Herms, A & Lukas, G (2007) Experimental validation of the ns-2 wireless model using simulation, emulation, and real network, In 4th Workshop on Mobile Ad-Hoc Networks (WMAN07, pp 433–444 Jacobson, V ( 199 0) Compressing TCP/IP Headers for Low-Speed... It required a small adjustment on the module for use in version 2. 29, and the structure of the wireless node of NS-2, because the original module only applies to wired networks Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks 17 201 Fig 6 Label routing performed by MPLS 4.5.1 Factors...Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks 15 199 Fig 5 Cooperative solution: compression + aggregation enough to offer significant gains in comparison to more robust algorithms Therefore, it is... Conference on Winter Simulation, pp 157–1 69 Schmidt-Eisenlohr, F., Letamendia-Murua, J., Torrent-Moreno, M & Hartenstein, H (2006) Bug Fixes on the IEEE 802.11 DCF module of the Network Simulator Ns-2.28, Technical Report URL: http://www.telematica.polito.it/fiore/index.html 24 208 Wireless Mesh networks Wireless Mesh Networks Schruben, L., Singh, H & Tierney, L ( 198 3) Optimal tests for initialization bias... Interfaces, CRC Press, chapter 5, pp 83 99 10 Virtual Home Region Multi-hash Location Management Service (VIMLOC) for Large-Scale Wireless Mesh Networks1 J Mangues-Bafalluy, M Requena-Esteso, J Núñez-Martínez and A Krendzel Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) Av Carl Friedrich Gauss, 7 – 08860 Castelldefels – Barcelona Spain 1 Introduction Wireless mesh networks (WMNs) have recently received... keeps generating samples Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks Algorithm Robust Header Compression (RoHC) Static Header Compression (SHC) Static Header Compression + Aggregation (SHC+AG) 19 203 Compression gain 0.8645 0.6384 0.8274 Table 2 Compression gain of the header compression... SIGMOBILE Mob Comput Commun Rev 9( 4): 50–61 Law, A M & McComas, M G ( 199 1) Secrets of successful simulation studies, Proceedings of the 23rd conference on Winter simulation, IEEE Computer Society, Washington, DC, USA, pp 21–27 L’Ecuyer, P ( 199 9) Good parameters and implementations for combined multiple recursive random number generators, Operations Research 47(1): 1 59 164 L’Ecuyer, P (2001) Software... aggregated packets In this case, we can say that the compression gain of this approach is also influenced by the aggregation degree, which in our experiments 20 204 Wireless Mesh networks Wireless Mesh Networks Calls performed from leaves None RoHC SHC 90 SHC+AG 80 Packet loss (%) 70 60 50 40 30 20 10 2 3 4 5 6 Number of simultaneous calls 7 8 Fig 8 Packet loss of calls performed over the tree scenario, using . multiple recursive generator (CMRG) (L’Ecuyer, 199 9). 193 Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks 10 Wireless Mesh networks Essentially, a master process (Akmaster). The latter approach, known as sequential 192 Wireless Mesh Networks Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks 9 procedure, gather observations at the output. in the model. 190 Wireless Mesh Networks Pursuing Credibility in Performance Evaluation of VoIP Over Wireless Mesh Networks 7 – Sensitivity analysis, that is, the investigation of potential changes